{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "81ae1a8a",
   "metadata": {},
   "source": [
    "### 一、导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d487ec2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9d1686c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1444037 entries, 0 to 1444036\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count    Dtype  \n",
      "---  ------         --------------    -----  \n",
      " 0   user_id        1444037 non-null  int64  \n",
      " 1   merchant_id    1444037 non-null  int64  \n",
      " 2   coupon_id      865468 non-null   float64\n",
      " 3   discount_rate  865468 non-null   object \n",
      " 4   distance       1444037 non-null  int64  \n",
      " 5   date_received  865468 non-null   float64\n",
      " 6   date           639086 non-null   float64\n",
      "dtypes: float64(3), int64(3), object(1)\n",
      "memory usage: 77.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_train=pd.read_csv(r\"F:\\大三上\\数据分析与数据挖掘\\09\\小课\\数据集\\train.csv\")\n",
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac4958bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 204844 entries, 0 to 204843\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   user_id        204844 non-null  int64  \n",
      " 1   merchant_id    204844 non-null  int64  \n",
      " 2   coupon_id      81811 non-null   float64\n",
      " 3   discount_rate  81811 non-null   object \n",
      " 4   distance       204844 non-null  int64  \n",
      " 5   date_received  81811 non-null   float64\n",
      " 6   date           129681 non-null  float64\n",
      "dtypes: float64(3), int64(3), object(1)\n",
      "memory usage: 10.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data_test=pd.read_csv(r\"F:\\大三上\\数据分析与数据挖掘\\09\\小课\\数据集\\test.csv\")\n",
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a056440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>date_received</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.444037e+06</td>\n",
       "      <td>1.444037e+06</td>\n",
       "      <td>865468.000000</td>\n",
       "      <td>1.444037e+06</td>\n",
       "      <td>8.654680e+05</td>\n",
       "      <td>6.390860e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.689131e+06</td>\n",
       "      <td>4.103973e+03</td>\n",
       "      <td>6998.555189</td>\n",
       "      <td>2.492586e+00</td>\n",
       "      <td>2.016029e+07</td>\n",
       "      <td>2.016035e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.123587e+06</td>\n",
       "      <td>2.407392e+03</td>\n",
       "      <td>4156.569793</td>\n",
       "      <td>3.558444e+00</td>\n",
       "      <td>1.593196e+02</td>\n",
       "      <td>1.421340e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.016010e+07</td>\n",
       "      <td>2.016010e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.843454e+06</td>\n",
       "      <td>2.099000e+03</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.016013e+07</td>\n",
       "      <td>2.016022e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.696004e+06</td>\n",
       "      <td>3.532000e+03</td>\n",
       "      <td>7610.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.016021e+07</td>\n",
       "      <td>2.016033e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.528249e+06</td>\n",
       "      <td>6.424000e+03</td>\n",
       "      <td>10323.000000</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>2.016042e+07</td>\n",
       "      <td>2.016051e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.361032e+06</td>\n",
       "      <td>8.856000e+03</td>\n",
       "      <td>14045.000000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>2.016053e+07</td>\n",
       "      <td>2.016063e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id   merchant_id      coupon_id      distance  date_received  \\\n",
       "count  1.444037e+06  1.444037e+06  865468.000000  1.444037e+06   8.654680e+05   \n",
       "mean   3.689131e+06  4.103973e+03    6998.555189  2.492586e+00   2.016029e+07   \n",
       "std    2.123587e+06  2.407392e+03    4156.569793  3.558444e+00   1.593196e+02   \n",
       "min    4.000000e+00  1.000000e+00       1.000000  0.000000e+00   2.016010e+07   \n",
       "25%    1.843454e+06  2.099000e+03    3200.000000  0.000000e+00   2.016013e+07   \n",
       "50%    3.696004e+06  3.532000e+03    7610.000000  1.000000e+00   2.016021e+07   \n",
       "75%    5.528249e+06  6.424000e+03   10323.000000  4.000000e+00   2.016042e+07   \n",
       "max    7.361032e+06  8.856000e+03   14045.000000  1.000000e+01   2.016053e+07   \n",
       "\n",
       "               date  \n",
       "count  6.390860e+05  \n",
       "mean   2.016035e+07  \n",
       "std    1.421340e+02  \n",
       "min    2.016010e+07  \n",
       "25%    2.016022e+07  \n",
       "50%    2.016033e+07  \n",
       "75%    2.016051e+07  \n",
       "max    2.016063e+07  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86388690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>缺失值</th>\n",
       "      <th>最大值</th>\n",
       "      <th>最小值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <td>0</td>\n",
       "      <td>7361032.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_id</th>\n",
       "      <td>0</td>\n",
       "      <td>8856.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coupon_id</th>\n",
       "      <td>578569</td>\n",
       "      <td>14045.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_rate</th>\n",
       "      <td>578569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_received</th>\n",
       "      <td>578569</td>\n",
       "      <td>20160531.0</td>\n",
       "      <td>20160101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>804951</td>\n",
       "      <td>20160630.0</td>\n",
       "      <td>20160101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  缺失值         最大值         最小值\n",
       "user_id             0   7361032.0         4.0\n",
       "merchant_id         0      8856.0         1.0\n",
       "coupon_id      578569     14045.0         1.0\n",
       "discount_rate  578569         NaN         NaN\n",
       "distance            0        10.0         0.0\n",
       "date_received  578569  20160531.0  20160101.0\n",
       "date           804951  20160630.0  20160101.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#返回缺失值数、最大值、最小值\n",
    "#描述性统计分析\n",
    "explore_train=data_train.describe(percentiles=[],include='all').T\n",
    "explore_train['null']=data_train.isnull().sum()\n",
    "explore_train=explore_train[['null','max','min']]\n",
    "explore_train.columns=['缺失值','最大值','最小值']\n",
    "explore_train#train 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "643d26a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>缺失值</th>\n",
       "      <th>最大值</th>\n",
       "      <th>最小值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <td>0</td>\n",
       "      <td>7360967.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_id</th>\n",
       "      <td>0</td>\n",
       "      <td>8856.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coupon_id</th>\n",
       "      <td>123033</td>\n",
       "      <td>14045.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_rate</th>\n",
       "      <td>123033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_received</th>\n",
       "      <td>123033</td>\n",
       "      <td>20160615.0</td>\n",
       "      <td>20160601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>75163</td>\n",
       "      <td>20160630.0</td>\n",
       "      <td>20160601.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  缺失值         最大值         最小值\n",
       "user_id             0   7360967.0         4.0\n",
       "merchant_id         0      8856.0         1.0\n",
       "coupon_id      123033     14045.0         1.0\n",
       "discount_rate  123033         NaN         NaN\n",
       "distance            0        10.0         0.0\n",
       "date_received  123033  20160615.0  20160601.0\n",
       "date            75163  20160630.0  20160601.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#返回缺失值数、最大值、最小值\n",
    "#描述性统计分析\n",
    "explore_test=data_test.describe(percentiles=[],include='all').T\n",
    "explore_test['null']=data_test.isnull().sum()\n",
    "explore_test=explore_test[['null','max','min']]\n",
    "explore_test.columns=['缺失值','最大值','最小值']\n",
    "explore_test#test 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fff8ccad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存结果\n",
    "explore_train.to_csv('explore_train.csv')\n",
    "explore_test.to_csv('explore_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "588f47c8",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a8d644f550>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(explore_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "213f206a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a8dc43a400>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(explore_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b52be83",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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